Methods and apparatus to determine a unique audience for internet-based media

ABSTRACT

Methods, apparatus, systems and articles of manufacture are disclosed to generate measures of unique audiences for Internet-based media. An example apparatus disclosed herein includes at least one memory, instructions, and at least one processor. The processor to execute the instructions to: receive first HyperLogLog (HLL) data from a first server of a first database proprietor and second HyperLogLog (HLL) data from a second server of a second database proprietor, the first HLL data including obfuscated first user impression data and the second HLL data including obfuscated second user impression data; generate union HLL data based on the first HLL data from the first database proprietor and the second HLL data from the second database proprietor by performing a union of data sets of the obfuscated first user impression data represented in the first HLL data and the obfuscated second user impression data represented in the second HLL data; and determine a total number of deduplicated unique audience members based on the union HLL data generated by the vector analyzer.

RELATED APPLICATIONS

This patent arises from a continuation application of U.S. patentapplication Ser. No. 16/520,100, filed on Jul. 23, 2019, which claimspriority to, and the benefit of, U.S. Provisional Patent ApplicationSer. No. 62/702,734, filed on Jul. 24, 2019. U.S. patent applicationSer. No. 16/520,100 and U.S. Provisional Patent Application Ser. No.62/702,734 are hereby incorporated herein by reference in theirentireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to computing systems, and, moreparticularly, to computing systems to generate measures of uniqueaudiences for Internet-based media.

BACKGROUND

In recent years, media devices have been provided with Internetconnectivity and the ability to retrieve media from the Internet. Assuch, media exposure has shifted away from conventional methods ofpresentation, such as broadcast television, towards presentation viaconsumer devices accessing media via the Internet.

Media providers and/or other entities such as, for example, advertisingcompanies, broadcast networks, etc. are often interested in the viewing,listening, and/or media behavior of audience members and/or the publicin general. The media usage and/or exposure habits of monitored audiencemembers, as well as demographic data about the audience members, arecollected and used to statistically determine the size and demographicsof an audience of interest.

Traditionally, audience measurement entities determine audienceengagement levels for media programming and/or advertisements based onregistered panel members. That is, an audience measurement entityenrolls people who consent to being monitored into a panel. The audiencemeasurement entity then monitors those panel members to collect mediameasurement data identifying media (e.g., television programs, radioprograms, movies, DVDs, etc.) presented to those panel members. In thismanner, the audience measurement entity can determine exposure measuresfor different media (e.g., content and/or advertisements) based on thecollected media measurement data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in which theteachings of this disclosure may be implemented.

FIG. 2 is a block diagram of an example implementation of an exampledatabase proprietor (DP) audience metrics generator at the exampledatabase proprietors of FIG. 1 that may be used to generate audiencemeasurement data as hyperloglog (HLL) data that protects the privaciesof audience members.

FIG. 3 is a block diagram of an example implementation of the AMEaudience metrics generator of FIG. 1 to process HLL data from thedatabase proprietors of FIGS. 1 and 2 to generate audience sizes ofmedia.

FIG. 4 is an example diagram illustrating generating of HLL data by theDP audience metrics generator of FIG. 2, and processing of the HLL databy the audience metrics generator of FIGS. 1 and 3.

FIG. 5 is another example diagram illustrating generating of HLL data bythe DP audience metrics generator of FIG. 2, and processing of the HLLdata by the audience metrics generator of FIGS. 1 and 3.

FIG. 6 is an example diagram illustrating a manner in which the audiencedeterminer of FIG. 3 determines a normalized harmonic mean based onmaximum uniqueness estimates of the HLL data from the from the databaseproprietors of FIGS. 1 and 2.

FIG. 7 is an example graph showing the reduction of processingcomplexity using the examples disclosed herein.

FIG. 8 is a flowchart representative of machine-readable instructionswhich may be executed to implement the example DP audience metricsgenerator(s) of the first database proprietor and/or the second databaseproprietor of FIGS. 1 and 2.

FIG. 9 is a flowchart representative of machine-readable instructionswhich may be executed to implement the example audience metricsgenerator of FIGS. 1 and 3.

FIG. 10 is a block diagram of an example processing platform structuredto execute the instructions of FIG. 8 to implement the example DPaudience metrics generator(s) of the first database proprietor and/orthe second database proprietor of FIGS. 1-2.

FIG. 11 is a block diagram of an example processing platform structuredto execute the instructions of FIG. 9 to implement the example AMEaudience metrics generator of the audience measurement entity of FIGS. 1and 3.

The figures are not to scale. In general, the same reference numberswill be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts. Connection references(e.g., attached, coupled, connected, and joined) are to be construedbroadly and may include intermediate members between a collection ofelements and relative movement between elements unless otherwiseindicated. As such, connection references do not necessarily infer thattwo elements are directly connected and in fixed relation to each other.

Descriptors “first,” “second,” “third,” etc. are used herein whenidentifying multiple elements or components which may be referred toseparately. Unless otherwise specified or understood based on theircontext of use, such descriptors are not intended to impute any meaningof priority, physical order or arrangement in a list, or ordering intime but are merely used as labels for referring to multiple elements orcomponents separately for ease of understanding the disclosed examples.In some examples, the descriptor “first” may be used to refer to anelement in the detailed description, while the same element may bereferred to in a claim with a different descriptor such as “second” or“third.” In such instances, it should be understood that suchdescriptors are used merely for ease of referencing multiple elements orcomponents.

DETAILED DESCRIPTION

Techniques for monitoring user access to an Internet-accessible media,such as digital television (DTV) media and digital content ratings (DCR)media, have evolved significantly over the years. Internet-accessiblemedia is also known as digital media. In the past, such monitoring wasdone primarily through server logs. In particular, entities servingmedia on the Internet would log the number of requests received fortheir media at their servers. Basing Internet usage research on serverlogs is problematic for several reasons. For example, server logs can betampered with either directly or via zombie programs, which repeatedlyrequest media from the server to increase the server log counts. Also,media is sometimes retrieved once, cached locally and then repeatedlyaccessed from the local cache without involving the server. Server logscannot track such repeat views of cached media. Thus, server logs aresusceptible to both over-counting and under-counting errors.

The inventions disclosed in Blumenau, U.S. Pat. No. 6,108,637, which ishereby incorporated herein by reference in its entirety, fundamentallychanged the way Internet monitoring is performed and overcame thelimitations of the server-side log monitoring techniques describedabove. For example, Blumenau disclosed a technique wherein Internetmedia to be tracked is tagged with monitoring instructions. Inparticular, monitoring instructions (also known as a media impressionrequest) are associated with the hypertext markup language (HTML) of themedia to be tracked. When a client requests the media, both the mediaand the impression request are downloaded to the client. The impressionrequests are, thus, executed whenever the media is accessed, be it froma server or from a cache.

Impression requests cause monitoring data reflecting information aboutan access to the media to be sent from the client that downloaded themedia to a monitoring entity via a cookie. Sending the monitoring datafrom the client to the monitoring entity is known as an impressionrequest. Typically, the monitoring entity is an audience measuremententity (AME) that did not provide the media to the client and who is atrusted (e.g., neutral) third party for providing accurate usagestatistics (e.g., The Nielsen Company, LLC).

There are many database proprietors operating on the Internet. Thesedatabase proprietors provide services to large numbers of subscribers.In exchange for the provision of services, the subscribers register withthe database proprietors. Examples of such database proprietors includesocial network sites (e.g., Facebook, Twitter, MySpace, etc.),multi-service sites (e.g., Yahoo!, Google, Axiom, Catalina, etc.),online retailer sites (e.g., Amazon.com, Buy.com, etc.), creditreporting sites (e.g., Experian), streaming media sites (e.g., YouTube,Hulu, etc.), etc. These database proprietors set cookies and/or otherdevice/user identifiers on the client devices of their subscribers toenable the database proprietor to recognize their subscribers when theyvisit their web site.

The protocols of the Internet make cookies inaccessible outside of thedomain (e.g., Internet domain, domain name, etc.) on which they wereset. Thus, a cookie set in, for example, the facebook.com domain isaccessible to servers in the facebook.com domain, but not to serversoutside that domain. Therefore, although an AME might find itadvantageous to access the cookies set by the database proprietors, theyare unable to do so.

The inventions disclosed in Mainak et al., U.S. Pat. No. 8,370,489,which is incorporated by reference herein in its entirety, enable an AMEto leverage the existing databases of database proprietors to collectmore extensive Internet usage by extending the impression requestprocess to encompass partnered database proprietors and by using suchpartners as interim data collectors. The inventions disclosed in Mainaket al. accomplish this task by structuring the AME to respond toimpression requests from clients (who may not be a member of an audiencemember panel and, thus, may be unknown to the audience member entity) byredirecting the clients from the AME to a database proprietor, such as asocial network site partnered with the audience member entity, using animpression response. Such a redirection initiates a communicationsession between the client accessing the tagged media and the databaseproprietor. For example, the impression response received from the AMEmay cause the client to send a second impression request to the databaseproprietor. In response to receiving this impression request, thedatabase proprietor (e.g., Facebook) can access any cookie it has set onthe client to thereby identify the client based on the internal recordsof the database proprietor. In the event the client corresponds to asubscriber of the database proprietor, the database proprietorlogs/records a database proprietor demographic impression in associationwith the client/user.

As used herein, an impression is defined to be an event in which a homeand individual access and is exposed to media (e.g., an advertisement,content, a group of advertisements and/or a collection of content). InInternet media delivery, a quantity of impressions or impression countis the total number of times media (e.g., content, an advertisement, oradvertisement campaign) has been accessed by a web population (e.g., thenumber of times the media is accessed). In some examples, an impressionor media impression is logged by an impression collection entity (e.g.,an AME or a database proprietor) in response to an impression requestfrom a user/client device that requested the media. For example, animpression request is a message or communication (e.g., an HTTP request)sent by a client device to an impression collection server to report theoccurrence of a media impression at the client device. In some examples,a media impression is not associated with demographics. In non-Internetmedia delivery, such as television (TV) media, a television or a deviceattached to the television (e.g., a set-top-box or other mediamonitoring device) may monitor media being output by the television. Themonitoring generates a log of impressions associated with the mediadisplayed on the television. The television and/or connected device maytransmit impression logs to the impression collection entity to log themedia impressions.

A user of a computing device (e.g., a mobile device, a tablet, a laptop,etc.) and/or television may be exposed to the same media via multipledevices (e.g., two or more of a mobile device, a tablet, a laptop, etc.)and/or via multiple media types (e.g., digital media available online,digital TV (DTV) media temporality available online after broadcast, TVmedia, etc.). For example, a user may start watching the Walking Deadtelevision program on a television as part of TV media, pause theprogram, and continue to watch the program on a tablet as part of DTVmedia. In such an example, the exposure to the program may be logged byan AME twice, once for an impression log associated with the televisionexposure, and once for the impression request generated by a censusmeasurement science (CMS) tag executed on the tablet. Multiple loggedimpressions associated with the same program and/or same user aredefined as duplicate impressions. Duplicate impressions are problematicin determining total reach estimates because one exposure via two ormore cross-platform devices may be counted as two or more uniqueaudience members. As used herein, reach is a measure indicative of thedemographic coverage achieved by media (e.g., demographic group(s)and/or demographic population(s) exposed to the media). For example,media reaching a broader demographic base will have a larger reach thanmedia that reached a more limited demographic base. The reach metric maybe measured by tracking impressions for known users (e.g., panelists ornon-panelists) for which an audience measurement entity storesdemographic information or can obtain demographic information.Deduplication is a process that is used to adjust cross-platform mediaexposure totals so that a single audience member is not counted multipletimes for multiple exposures to the same media delivered/accessed viadifferent media-delivery platforms.

As used herein, a unique audience (e.g., a unique audience size,deduplicated audience size, or audience size) is based on audiencemembers distinguishable from one another. That is, a particular audiencemember exposed to particular media is measured as a single uniqueaudience member regardless of how many times that audience member isexposed to that particular media. If that particular audience member isexposed multiple times to the same media, the multiple exposures for theparticular audience member to the same media is counted as only a singleunique audience member. In this manner, impression performance forparticular media is not disproportionately represented when a smallsubset of one or more audience members is exposed to the same media anexcessively large number of times while a larger number of audiencemembers is exposed fewer times or not at all to that same media. Bytracking exposures to unique audience members, a unique audience measuremay be used to determine a reach measure to identify how many uniqueaudience members are reached by media. In some examples, increasingunique audience and, thus, reach, is useful for advertisers wishing toreach a larger audience base.

An AME may want to find unique audience/deduplicate impressions acrossmultiple database proprietors (DPs), custom date ranges, customcombinations of assets and platforms, etc. Some deduplication techniquesused by an AME perform deduplication across DPs using additional systems(e.g., Audience Link, etc.). For example, such deduplication techniquesmatch or probabilistically link personally identifiable information(PII) from each source. Such deduplication techniques require storing orexporting massive amounts of user data, using approximations instead ofdirect measurement, or calculating audience overlap for all possiblecombinations, neither of which are desirable. PII data can be used torepresent and/or access audience demographics (e.g., geographiclocations, ages, genders, etc.)

Examples disclosed herein perform a HyperLogLog (HLL) deduplicationtechnique to perform dynamic deduplication. HLL allows an AME to obtainthe non-PII HLL outputs from various DPs, thereby allowing deduplicationto be performed (a) on-the-fly, (b) using a fraction of thestorage/computation of conventional deduplication techniques, (c)without sacrificing user or client privacy, and (d) using any number ofdata sources or assets. Examples disclosed herein dynamically duplicateacross many data sources to allow an AME to improve non-coverage, kids'measurement, and expanded demographics. The AME can improve non-coverageby combining multiple data sources so that the likelihood a person isnot covered by an AME measurement is decreased and overlap betweenproviders (e.g., database proprietors) can be seen. The AME can improvekids' measurement by, if data sources with high kids' coverage anduseful PII fields for matching are identified, confirming kids'measurement by multiple database proprietors. The AME can improveexpanded demographics when database proprietors with more specificdemographic data are incorporated through direct matching and comparisonwith other database proprietors.

HLL is an algorithm for estimating unique counts. An HLL processincludes sorting hashed values of data into a plurality of bins (e.g., mbins) of HLL data. To form the HLL data hash values are assigned to binsbased on a shared chunk or a shared number of bits (e.g., leading bits,or a number of most significant bits (MSBs)) across multiple hashvalues. The shared number of bits define an address (e.g., identicalvalues get assigned to the same bin) of a corresponding bin in the HLLdata. Multiple bins in the HLL data are defined by corresponding leadingbits of the hash values. More bins in the HLL data correspond to moregranularity, less noisy estimates, and more computer memory usage. Eachhash value is used to update a bin's estimate of the overall uniqueaudience member count represented by that bin of the HLL data. In thismanner, the estimate of a unique audience size is also based on the hashvalues themselves. When a database proprietor receives the HLL datastructure from each DP, the database proprietary can combine themultiple data structures into a single data structure, which will allowto compute an estimate for the union of all the DP audiences based on amodified average of the audience size estimate resulting therefrom. Morespecifically, the AME can combine the data structures from each DP bycomputing a new HLL data structure where only the hash informationcorresponding to the largest estimate saved. In this manner, the AMEuses the union to determine union HLL data based on the maximumuniqueness values between the bins of the HLL data sets. The AME thendetermines a normalized harmonic mean across all bins' estimates of theunion HLL data set to calculate a final estimate of the overall uniqueaudience member count.

Example HLL-based techniques disclosed herein may be used to count thenumber of unique items in a data stream (e.g., the number of uniquepanelist IDs, the number of database proprietor subscriber user IDs,etc. to determine unique audience counts or unique audience sizes). Someconventional methods to count unique items (e.g., unique audiencemembers) include tracking each new value while reading through data andcounting the number of unique values. However, such conventionaltechniques require a lot of computer memory to store the unique items.Additionally, previous unique counts (e.g., for unique audience members)cannot be reused when new impression data is received. Accordingly, newimpression data requires recounting. Example HLL-based techniquesdisclosed herein may be used to directly address the limitations of suchconventional techniques.

In some examples disclosed herein, HLL-based techniques can be used todeduplicate audience member data sets. For example, examples disclosedherein may perform deduplication using HLL “Building-Blocks.” An AME cancreate HLL building-blocks by using a common PII input type and/orformat across all DPs, assets, date ranges, etc. to create hash valuesby hashing PII input data (e.g., email addresses, phone numbers, acommon concatenation of other PII information such as Name and Zip andEmail, etc.). Using a common PII input type and/or format across allDPs, assets, data ranges, etc. to hash ensures values will be binnedtogether correctly in corresponding bias (e.g., for differentdemographics, for PII, etc.). For example, a plurality of HLL bins(e.g., m HLL bins) may be created at the most granular level to bemeasured (e.g., m bins for each site, day, demographic buckets, etc.).

In examples disclosed herein, a salt may be used to improve the privacyprotections of the PII input data. In this manner, examples disclosedherein, use a salt to generate hash values of input PII data at adatabase proprietor to substantially reduce or eliminate the likelihoodof an intermediary third party from revealing or discovering theunderlying input PII data corresponding to the hash values, whichresults from the reliance of the HLL data set on individual hash valuesto compute the HLL data structure. As used herein, a salt is random data(e.g., an arbitrary data, etc.) used by a hashing algorithm (e.g., anHLL algorithm, etc.). In some examples, a salt can be generated usingpseudo-random inputs. In some examples, a generated salt can beconcatenated with the data (e.g., demographic information, PII data,etc.) to be hashed. In such examples, when the concatenated string ishashed, the resulting hash value is varied based on the salt used.Accordingly, the underlying data is concealed by the salt. In suchexamples, the salt and hash value are stored in a hashing database. Insome examples, the salt can be reused as needed. In other examples, anew salt can be generated for each instance of data to be hashed.

HLL processes may lead to deduplication errors that the AME may need tomitigate. For example, a deduplication error may stem from thegranularity of the bins. For example, fewer/larger bins produce highervariance unique estimates than smaller ones. In such examples, the AMEmust determine the m number of bins that correspond to an amount ofacceptable error. In another example, a deduplication error may stemfrom the choice of the PII input (e.g., for multi-DP deduplication). DPsmay have mismatched inputs for the same person or low coverage for aparticular PII field. For example, if a chosen PII field is an emailaddress and one person has different email addresses for two DPs, theperson may be double-counted. In another example, if a DP (e.g.,Facebook) only has email addresses of subscribers for 70% of loggedimpressions, and only 70% of people have Facebook, the additionalnon-coverage (e.g., for logged impressions corresponding to people notrecognized by Facebook) may need to be corrected.

In some examples, HLL may be limited by an ability to match PII. In someexample, an AME may overcome such limitation by supplementing the PIIdata with Audience Link (AL) panel data which includes PII data ofpanelists enrolled into an AL panel maintained by the AME. When a personenrolls in the AL panel, the person, or panelist, agrees to provideaccess to their PII by the AME for audience metrics. In some examples,when all PII fields of AL panel data of the AME matches all fields ofPII data of a DP, the AL panel data and the HLL data can be madeequivalent. For example, if m is set to a number of AL panelists andfields of AL panelist PII data for those AL panelists match fields ofPII data of the DP PII data, then the AL panelist can be set to the HLLdata. In some examples, it may be rare for PII to be matching for allfields. In such cases, AL may use machine learning to predict, given twosets of PII inputs, the probability of a match. When HLL does not havePII from all DPs directly available to compare, compression methods maybe trained (if applicable), tested, and deployed using a one-sideddataset. Without compression, using more fields makes matching harder.Yet, using more fields increases our confidence in a true match,corresponding to fewer other adjustments, as further described below.

Some examples disclosed herein mitigate PII input error. An AME maycorrect PII input error using several approaches. One approach is tocreate robust PII inputs. Relying on one field of PII to produce thesame hashed value across all DPs is susceptible to error. Aconcatenation of several fields may be more robust. For example,Jake_Dailey_jd123@nlsn.com_94129 has more in common withJake_Dailey_goblue123@gmail.com_94129 than email alone (e.g.,jd123@nlsn.com and goblue123@gmail.com) because first and last names areprepended to the email addresses and a zip code is appended to the emailaddresses. A lossy compression algorithm can capture the most relevantaspects of this string. For example, locality-sensitive hashing maycreate a compressed representation with the most significant prices ofinput and use this to determine similarity. Siamese Neural Networks aredesired to learn a matching compressed representation for similar butdifferent inputs.

A second approach to mitigating PII input error corresponds tonon-coverage adjustment using DP and AME data. Each PII field's coverageis likely skewed toward a non-representative subset of the population.Unlike overall DP non-coverage (which an AME adjusts for using panel andsurvey data), coverage error for PII inputs can be measured directlyusing the demographic data they already have, which may be supplementedwith AME panel data, where possible. If an AME and DP can measure thisskew, this subset can be reweighted using the observed non-coveragerate.

A third approach to mitigating PII input error corresponds to PIIfield-specific corrections based on survey and panel inputs. Each PIIfield has its own errors associated with it. For example, a person mayuse multiple emails across different DP websites. Alternatively,multiple people may share the same phone number used across websites. Insome examples, an AME has panel data on some input fields and mayconduct surveys to account for misattribution or overestimationresulting from a specific choice of PII field. This methodology mayfollow a model of an AME's digital adjustment factor methodology. Insome examples, the three above-approaches may be combined to mitigateerror with the fewest assumptions.

Assuming a set, S, of non-negative uniformly distributed random numbers(e.g., {x: x˜U(a, b), a≥0}), every x can be mapped to its binaryrepresentation (e.g., 4 can be mapped to 100). Because x is uniformlydistributed, if the maximum number of leading zeros in the binaryrepresentation is known, an estimate is known for how many elements arein the set, S. For example, if the maximum number of leading 0's is 3(000), then there will be 2³=8 elements in S (e.g., in base 2: 000, 001,010, 011, 100, 101, 110, 111 or in base 10: 0, 1, 2, 3, 4, 5, 6, 7).

Assuming the data is non-uniform or non-numeric, a hash function may beused to uniformly assign unique numbers to each unique value in a hashdata set. For example, using two unique strings, “a” and “b,” a 1-bithash function may be used to assign “a” to 0 and “b” to 1. Based on theabove observation corresponding to the maximum number of leading zeros,there are 2¹ unique values. With a 32-bit or 64-bit hash function, lotsof unique values can be represented and need to find the maximum numberof leading zeros to have an estimate of how much uniqueness is in thedata.

While a 32-bit hash function gives the ability to represent many uniquevalues without re-using hash values, some examples may have much lessthan 2³² unique values (e.g., when the data is sparse in the hashspace). In such examples, there is a high variance in the count based onthe specific choice of hashing function (or, which hash values are beingassigned). HLL addresses this variance problem. Hashed values are sortedinto subsets using an address and a uniqueness estimate (e.g., themaximum number of leading 0's is found in each subset to determine theuniqueness estimate), and the harmonic mean is taken across subsets,thereby allowing correction of the overestimates using underestimates.In some examples, the subsets are arbitrarily small to get moreprecision. In some examples, if (# of unique values)<5/2*(# of subsets),HLL may be biased and Linear Counting may be used.

HLL sorts hashed values into subsets using an address based on thevalue's binary representation. For example, new values are added toexisting subsets and may be checked if any have more leading zeros thanthe previous maximum unique estimate of that subset. HLL takes aharmonic mean across subsets. For example, if the estimates frompreviously seen subsets are saved, subsets may be added or removed thatan AME would like to include in a unique count. Taking the aboveexamples together, the conditions of needing to see the entire datasetand saving every unique value to estimate uniqueness is removed, amongstother valuable applications. Examples of the HLL algorithm are describedbelow in conjunction with FIGS. 4-6.

The HLL technique hashes unique values to unique elements of a uniformlydistributed set of integers and maps those integers into their binaryrepresentation. Further, the HLL technique sorts binary values intosubsets using an address based on each value. Additionally, the HLLtechnique finds the maximum number of leading 0's in a binary valuewithin each subset as an estimate of uniqueness in the data. Forexample, the HLL technique may add new values to existing subsets andmay check if any have more leading zeros than the previous max of thesubset. The HLL technique finds the harmonic mean of uniquenessestimates across all subsets. For example, if the estimates frompreviously seen subsets were saved, the HLL technique adds or removessubsets that the user would like to include in the unique count. Takingthe above two examples together, the HLL technique removes thelimitations of needing to see the entire dataset and saving every uniquevalue to estimate uniqueness.

When producing a union HLL data set between two database proprietor HLLdata sets to generate a unique audience count, all bins with matchingaddresses across the database proprietor HLL data sets are comparedbetween building blocks of the database proprietor HLL data sets thatare to be deduplicated. This allows a finding of each bin's maximumunique estimate across the building blocks database proprietor HLL datasets. The harmonic mean is computed based on the bins' unique estimatesin the union HLL data sets.

HLL++ may be used as an implementation of HLL in examples disclosedherein. HLL++ uses a 64-bit hash function to reduce the change of twounique values having the same hashed value. Additionally, HLL++ adjustsfor bias in switching from Linear Counting to HLL. Additionally, HLL+uses a sparse representation of subsets, which is more memory efficient.

In some examples, HLL can be utilized to implement on-the-flydeduplication. For example, a DP or user can create HLL building blocksas inputs to deduplication across impressions or platforms. HLL can beimplemented within Spark (e.g., databricks) as approxCountDistinct andapprocQuantile. Creating HLL building blocks as inputs to deduplicationacross impressions or platforms is enabled by storing HLLsubsets/registers for each building block cut of data. In some examples,projections methodologies may be adapted to perform on-the-flycomputations. For example, HLL building blocks may be created as inputsto deduplication across impressions or platforms in digital add ratings(DAR) Desktop misattribution factors (e.g., truncated singular valuedecomposition (tSVD)). In such an example, an AME or a DP can createbuilding block inputs of HLL data for each campaign time period, site,etc. as data becomes available (e.g., hourly, daily, etc.). When areport is generated for a particular time period. The AME/DP calculatestSVD's panel inputs for each demographic using building blocks belongingto period/campaign/etc. Factors may be updated quarterly or based on anyother duration of time. The AME/DP calculates a DP frequency based onthe HLL data inputs from a DP, calculates a tSVD matrix, adjusts DPimpression sums using the tSVD matrix, and applies DP frequency toimpression sums. In some examples, non-coverage can be performed using asimilar methodology.

FIG. 1 shows an example operating environment 100 that includes anexample audience measurement entity (AME) 102, an example databaseproprietor A 106A, and example second database proprietor 106B, andexample client devices 108. The example AME 102 includes an example AMEcomputer 110 that implements an example audience metrics generator 112to determine audience sizes based on media impressions logged by thedatabase proprietors 106A, 106B. In the illustrated example of FIG. 1,the AME computer 110 may also implement an impression monitor system tolog media impressions reported by the client devices 108. In theillustrated example of FIG. 1, the client devices 108 may be stationaryor portable computers, handheld computing devices, smart phones,Internet appliances, and/or any other type of device that may beconnected to the Internet and capable of presenting media.

As used herein, an audience size defined as a number of deduplicated orunique audience members exposed to a media item of interest for audiencemetrics analysis. A deduplicated or unique audience member is one thatis counted only once as part of an audience size. Thus, regardless ofwhether a particular person is detected as accessing a media item onceor multiple times, that person is only counted once in the audience sizefor that media item. Audience size may also be referred to as uniqueaudience or deduplicated audience.

As used herein, a media impression is defined as an occurrence of accessand/or exposure to media 114 (e.g., an advertisement, a movie, a movietrailer, a song, a web page banner, etc.). Examples disclosed herein maybe used to monitor for media impressions of any one or more media types(e.g., video, audio, a web page, an image, text, etc.). In examplesdisclosed herein, the media 114 may be content and/or advertisements.Examples disclosed herein are not restricted for use with any particulartype of media. On the contrary, examples disclosed herein may beimplemented in connection with tracking impressions for media of anytype or form in a network.

In the illustrated example of FIG. 1, content providers and/oradvertisers distribute the media 114 via the Internet to users thataccess websites and/or online television services (e.g., web-based TV,Internet protocol TV (IPTV), etc.). In some examples, the media 114 isserved by media servers of the same internet domains as the databaseproprietors 106A, 106B. For example, the database proprietors 106A, 106Binclude corresponding database proprietor servers 118A, 118B that canserve media 114 to their corresponding subscribers via the clientdevices 108. Examples disclosed herein can be used to generate audiencemetrics data that measures audience sizes of media served by differentones of the database proprietors 106A, 106B. For example, the databaseproprietors 106A, 106B may use such audience metrics data to promotetheir online media serving services (e.g., ad server services, mediaserver services, etc.) to prospective clients. By showing audiencemetrics data indicative of audience sizes drawn by corresponding ones ofthe database proprietors 106A, 106B, the database proprietors 106A, 106Bcan sell their media serving services to customers interested indelivering online media to users.

The media 114 is then presented via the client devices 108. When themedia 114 is accessed by the client devices 108, the client devices 108send impression requests 122A, 122B to the database proprietor servers118A, 118B to inform the database proprietor servers 118A, 118B of themedia accesses. In this manner, the database proprietor servers 118A,118B can log media impressions in impression records of correspondingdatabase proprietor audience metrics databases 124A, 124B. In someexamples, the client devices 108 also send impression requests 122C tothe AME 102 so that the AME 102 can log census impressions in an AMEaudience metrics database 126. In the illustrated example of FIG. 1, thedatabase proprietors 106A, 106B log demographic impressionscorresponding to accesses by the client devices 108 to the media 114.Demographic impressions are impressions logged in association withdemographic information collected by the database proprietors 106A, 106Bfrom registered subscribers of their services. Also, in the illustratedexample of FIG. 1, the AME computer 110 logs census-level mediaimpressions corresponding to accesses by client devices 108 to media114. Census-level media impressions (e.g., census impressions) areimpressions logged regardless of whether demographic information isknown for those logged impressions. In some examples, the AME computer110 does not collect impressions, and examples disclosed herein arebased on audience data from impressions collected by the databaseproprietors 106A, 106B.

In some examples, the media 114 is encoded to include a media identifier(ID). The media ID may be any identifier or information that can be usedto identify the corresponding media 114. In some examples, the media IDis an alphanumeric string or value. In some examples, the media ID is acollection of information. For example, if the media 114 is an episode,the media ID may include program name, season number, and episodenumber. When the media 114 includes advertisements, such advertisementsmay be content and/or advertisements. The advertisements may beindividual, standalone ads and/or may be part of one or more adcampaigns. The ads of the illustrated example are encoded withidentification codes (i.e., data) that identify the associated adcampaign (e.g., campaign ID, if any), a creative type ID (e.g.,identifying a Flash-based ad, a banner ad, a rich type ad, etc.), asource ID (e.g., identifying the ad publisher), and/or a placement ID(e.g., identifying the physical placement of the ad on a screen). Insome examples, advertisements tagged with the monitoring instructionsare distributed with Internet-based media content such as, for example,web pages, streaming video, streaming audio, IPTV content, etc. As notedabove, methods, apparatus, systems, and/or articles of manufacturedisclosed herein are not limited to advertisement monitoring but can beadapted to any type of content monitoring (e.g., web pages, movies,television programs, etc.).

In some examples, the media 114 of the illustrated example is tagged orencoded to include monitoring or tag instructions, which arecomputer-executable monitoring instructions (e.g., Java, JavaScript, orany other computer language or script) that are executed by web browsersthat access the media 114 via, for example, the Internet. Execution ofthe monitoring instructions causes the web browser to send theimpression requests 122A, 122B, 122C (e.g., also referred to as tagrequests) to one or more specified servers of the AME 102, the databaseproprietor A 106A, and or the second database proprietor 106B. As usedherein, tag requests 122A, 122B, 122C are used by the client devices 108to report occurrences of media impressions caused by the client devicesaccessing the media 114. In the illustrated example, the tag requests122A, 122B includes user-identifying information that the databaseproprietors 106A, 106B can use to identify the subscriber that accessedthe media 114. For example, when a subscriber of the first databaseproprietor 106A logs into a server of the first database proprietor 106Avia a client device 108, the first database proprietor 106A sets adatabase proprietor cookie on the client device 108 and maps that cookieto the subscriber's identity/account information at the databaseproprietor server 118 a. In examples disclosed herein, subscriberidentity and/or subscriber account information includes personallyidentifiable information (PII) such as full name, street address,residence city and state, telephone numbers, email addresses, ages,dates of birth, social security numbers, demographic information, and/orany other person information provided by subscribers in exchange forservices from the database proprietors 106A, 106B. By having such PIIinformation mapped to database proprietor cookies, the first databaseproprietor 106A can subsequently identify the subscriber based on thedatabase proprietor cookie to determine when that user accesseddifferent media 114 and to log an impression in association withdemographics and/or other PII information of that user. In theillustrated example of FIG. 1, the impression requests 122A, 122Binclude database proprietor cookies of the client devices 108 to informthe database proprietors 106A, 106B of the particular subscribers thataccessed the media 114. In some examples, the AME 102 also sets AMEcookies in the client devices 108 to identify users that are enrolled ina panel of the AME 102 such that the AME 102 collects PII information ofpeople that agree to have their internet activities monitored by the AME102.

The tag requests 122A, 122B, 122C may be implemented using HTTPrequests. However, whereas HTTP requests are network communications thattraditionally identify web pages or other resources to be downloaded,the tag requests 122A, 122B, 122C of the illustrated example are networkcommunications that include audience measurement information (e.g., adcampaign identification, content identifier, and/or user identificationinformation) as their payloads. The server (e.g., the AME computer 110and/or the database proprietor servers 118A, 118B) to which the tagrequests 122A, 122B, 122C are directed is programmed to log occurrencesof impressions reported by the tag requests 122A, 122B, 122C. Furtherexamples of monitoring instructions (e.g., beacon instructions) and usesthereof to collect impression data are disclosed in U.S. Pat. No.8,370,489 entitled “Methods and Apparatus to Determine Impressions usingDistributed Demographic Information,” which is hereby incorporatedherein by reference in its entirety.

In other examples, in which the media 114 is accessed by apps on mobiledevices, tablets, computers, etc. (e.g., that do not employ cookiesand/or do not execute instructions in a web browser environment), an apppublisher (e.g., an app store) can provide a data collector in aninstall package of an app for installation at the client devices 108.When a client device 108 downloads the app and consents to theaccompanying data collector being installed at the client device 108 forpurposes of audience/media/data analytics, the data collector can detectwhen the media 114 is accessed at the client device 108 and causes theclient device 108 to send one or more of the impression requests 122A,122B, 122C to report the access to the media 114. In such examples, thedata collector can obtain user identifiers and/or device identifiersstored in the client devices 108 and send them in the impressionrequests 122A, 122B, 122C enable the database proprietors 106A, 106Band/or the AME 102 to log impressions. Further examples of using acollector in client devices to collect impression data are disclosed inU.S. Pat. No. 8,930,701 entitled “Methods and Apparatus to CollectDistributed User Information for Media Impressions and Search Terms,”and in U.S. Pat. No. 9,237,138 entitled “Methods and Apparatus toCollect Distributed User Information for Media Impressions and SearchTerms,” both of which are hereby incorporated herein by reference intheir entireties.

In the illustrated example, the database proprietors 106A, 106B wouldlike to collaborate with the AME 102 so that the AME 102 can operate asan independent party that measures and/or verifies audience measurementinformation pertaining to the media 114 accessed by the subscribers ofthe database proprietors 106A, 106B. However, the database proprietors106A, 106B desire to do so while protecting the privacies of theirsubscribers by not sharing or revealing subscriber identities,subscriber information, and/or any other subscriber PII information tooutside parties. In examples disclosed herein, to share impression datawith the AME 102 without revealing subscriber identities, subscriberinformation, and/or any other subscriber PII information, the databaseproprietors 106A, 106B process their collected impression data togenerate corresponding HLL data 132A, 132B as described below inconnection with FIG. 2. In the illustrated example, to generate such HLLdata 132A, 132B, the database proprietors 106A, 106B are providedcorresponding DP audience metrics generators 107A, 107B.

As used herein, HLL data is an arrangement of data generated by an HLLalgorithm and approximates the number of distinct elements (e.g., PIIinformation) associated with a database proprietor. In the illustratedexample, the HLL data is a vector bins storing uniqueness estimates ofhash values corresponding to those bins. In other examples, the HLL datacan be any other suitable data structure (e.g., a matrix, etc.). Inexamples disclosed herein, the HLL data is sorted into bins (e.g., alocation in a vector, etc.), and each bin corresponds to an addressbased on a common portion of hash values in that bin. In some examples,each set of HLL data 132A, 132B has an associated harmonic mean valueacross all bins of that HLL data 132A, 132B to calculate an overallunique count.

FIG. 2 is a block diagram of an example implementation of an example DPaudience metrics generator 107A, 107B at the example databaseproprietors of FIG. 1 that may be used to generate audience measurementdata as hyperloglog (HLL) data that protects the privacies of audiencemembers. The example first DP audience metrics generator 107A includesan example first impression detector 202A, an example first saltgenerator 206A, an example first HLL generator 208A, an example firstsalt encryptor 210A, an example first AME interface 212A, and an examplefirst database proprietor interface 214A. In the illustrated example,the DP audience metrics generator 107A is in communication with anexample first impression and PII database 204A. The example second DPaudience metrics generator 107B includes an example second impressiondetector 202B, an example second salt generator 206B, an example secondHLL generator 208B, an example second salt encryptor 210B, an examplesecond AME interface 212B, and example second database proprietorinterface 214B. In the illustrated example, the second DP audiencemetrics generator 107B is in communication with an example secondimpression and PII database 204B. The examples described below aredescribe in conjunction with the first database proprietor 106A (e.g.,the first DP audience metrics generator 107A of the first databaseproprietor 106A). However, any or all of the functions of the firstdatabase proprietor 106A can be performed by the second databaseproprietor 106B (e.g., the second DP audience metrics generator 107B ofthe second database proprietor 106B). For example, some or all of thefunctions of the first impression detector 202A can be implemented bythe second impression detector 202B.

The example first impression detector 202A collects user impressiondata. For example, the impression detector 202A can detect impressionrequests 122A transmitted to the first database proprietor 106A from theclient devices 108. In some examples, the impression detector 202A candetect (e.g., extract, decode, etc.) media ID corresponding to the media114 and associated with the impression request 122A. In some examples,the impression detector 202A can associate the impression request 122Awith PII information (e.g., addresses, ages, email addresses, username,dates of birth, social security numbers, demographic information, and/orany other person information, etc.). For example, the first impressionrequest 122A can include a cookie set by the database proprietor 106A inassociation with a client device 108. In this manner, the databaseproprietor 106A can associate the impression with PII information of asubscriber that was logged into a service of the database proprietor106A when the database proprietor 106A set the cookie in the clientdevice 108.

The example first impression and PII database 204A is a database thatcontains a data structure (e.g., a matrix, a vector, etc.) associatingdetected impressions with PII information of users of the first databaseproprietor 106A. For example, the first impression and PII database 204Acan include identifiers associated with each impression identified bythe first impression detector 202A (e.g., a media ID, a time, etc.) andPII information.

The example first salt generator 206A generates a salt to use to saltthe PII information associated with each impression in the firstimpression and PII database 204A. For example, the first salt generator206A can generate a salt using a campaign attribute (e.g., a date rangeassociated with an advertising campaign, etc.). In some examples, thefirst salt generator 206A can use a random number generator to generatea salt. In other examples, the first salt generator 206A can use anyother suitable means for generating a salt. In some examples, the firstsalt generator 206A generates a shared salt to be used by the seconddatabase proprietor 106B.

The example first HLL generator 208A generates the HLL data 132A basedon the detected impressions and the PII information stored in theimpression and PII database 204A. For example, the first HLL generator208A can generate HLL data in which each bin (e.g., a location in theHLL data structure, etc.) corresponds to none, one, or more hashes ofPII information associated with impressions detected in a time range(e.g., an hour, a day, a particular date range associated with anadvertising campaign, etc.). In some examples, to generate a hash basedon a PII input, the first HLL generator 208A concatenates the PII inputwith the salt generated by the first salt generator 206A before hashingthe PII input. In examples disclosed, the first HLL generator 208Aprocesses the HLL data 132A as binary values. In some examples, thefirst HLL generator 208A can generate a bin address (e.g., a leading setof bits of the generated hash, etc.) and a bin value (e.g., a trailingset of bits of the generated hash, etc.) for a hash value based on theMSBs of the hash value. In some examples, the first HLL generator 208Agenerates a uniqueness estimate value associated with each element ofthe HLL data. In some examples, the first HLL generator 208A generatesthe uniqueness estimate value based on the number of leading zeros(e.g., zeros in MSBs that start with zero) in each hash value. Examplefunctions of the first HLL generator 208A are described below in greaterdetail in conjunction with FIGS. 4-6.

The example first salt encryptor 210A encrypts the salt generated by thefirst salt generator 206A with an encryption key. For example, the firstsalt encryptor 210A can use an encryption key associated with the firstdatabase proprietor 106A, the second database proprietor 106B, and/orthe AME 102. In other examples, the first salt encryptor 210A can use apublic key to encrypt the salt. In some examples, the first saltencryptor 210A can be omitted. In such examples, the salt generated bythe first salt generator 206A is unencrypted.

The example first AME interface 212A allows the first databaseproprietor 106A of the illustrated example of FIGS. 1 and 2 tocommunicate with the AME 102. For example, the first AME interface 212Acan be implemented by wide area network communication hardware (e.g., agateway, a router, etc.). In some examples, the first AME interface 212Aenables the first HLL generator 208A to transmit the generated HLL data132A to the AME 102. In some examples, the first database proprietor106A to receive communications from the AME 102 (e.g., instructions torotate the salt, etc.). In some examples, the first AME interface 212Acan be absent. In such examples, the first database proprietor 106A cancommunicate with the AME 102 via any other suitable method (e.g.,communicating via physical records, communicating via a third part,communicating via the second database proprietor 106B, etc.).

The example first database proprietor interface 214A allows the firstdatabase proprietor 106A to communicate with the second databaseproprietor 106B and/or other database proprietors. For example, thefirst database proprietor interface 214A can be implemented by wide areanetwork communication hardware (e.g., a gateway, a router, etc.). Insome examples, the first database proprietor interface 214A enables thefirst salt generator 206A and/or the salt encryptor 210 to transmit agenerated salt, an encryption key to the second database proprietor106B, and/or any other communications. In some examples, the firstdatabase proprietor interface 214A can be absent. In such examples, thefirst database proprietor 106A can communicate with the second databaseproprietor 106B via any other suitable method (e.g., communicating viaphysical records, communicating via a third part, communicating via thesecond database proprietor 106B, etc.). In the illustrated example, thefirst AME interface 212A and the first database proprietor interface214A are illustrated as separate. In other examples, the first AMEinterface 212A and the first database proprietor interface 214A can beimplemented by a common interface.

While an example manner of implementing the first DP audience metricsgenerator 107A and the second DP audience metrics generator 107B of FIG.1 is illustrated in FIG. 2, one or more of the elements, processesand/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example first impression detector 202A, the example firstimpression and PII database 204A, the example first salt generator 206A,the example first HLL generator 208A, the example first salt encryptor210A, the example first AME interface 212A, the example first databaseproprietor interface 214A, the example second impression detector 202B,the example second impression and PII database 204B, the example secondsalt generator 206B, the example second HLL generator 208B, the examplesecond salt encryptor 210B, the example second AME interface 212B, theexample second database proprietor interface 214B, and/or moregenerally, the example DP audience metrics generator 107A of FIG. 2 maybe implemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample first impression detector 202A, the example first impression andPII database 204A, the example first salt generator 206A, the examplefirst HLL generator 208A, the example first salt encryptor 210A, theexample first AME interface 212A, the example first database proprietorinterface 214A, the example second impression detector 202B, the examplesecond impression and PII database 204B, the example second saltgenerator 206B, the example second HLL generator 208B, the examplesecond salt encryptor 210B, the example second AME interface 212B, theexample second database proprietor interface 214B and/or, moregenerally, the example DP audience metrics generators 107A, 107B of FIG.2 could be implemented by one or more analog or digital circuit(s),logic circuits, programmable processor(s), programmable controller(s),graphics processing unit(s) (GPU(s)), digital signal processor(s)(DSP(s)), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)). When reading any of the apparatus or system claimsof this patent to cover a purely software and/or firmwareimplementation, at least one of the example, first impression detector202A, the example first impression and PII database 204A, the examplefirst salt generator 206A, the example first HLL generator 208A, theexample first salt encryptor 210A, the example first AME interface 212A,the example first database proprietor interface 214A, the example secondimpression detector 202B, the example second impression and PII database204B, the example second salt generator 206B, the example second HLLgenerator 208B, the example second salt encryptor 210B, the examplesecond AME interface 212B, the example second database proprietorinterface 214B is/are hereby expressly defined to include anon-transitory computer readable storage device or storage disk such asa memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. including the software and/or firmware. Further still, theexample DP audience metrics generator 107A, 107B of FIG. 2 may includeone or more elements, processes and/or devices in addition to, orinstead of, those illustrated in FIG. 2, and/or may include more thanone of any or all of the illustrated elements, processes and devices. Asused herein, the phrase “in communication,” including variationsthereof, encompasses direct communication and/or indirect communicationthrough one or more intermediary components, and does not require directphysical (e.g., wired) communication and/or constant communication, butrather additionally includes selective communication at periodicintervals, scheduled intervals, aperiodic intervals, and/or one-timeevents.

FIG. 3 is a block diagram of an example implementation of the AMEaudience metrics generator of FIG. 1 to process HLL data from thedatabase proprietors of FIGS. 1 and 2 to generate audience sizes ofmedia. The example audience metrics generator 112 includes an exampledatabase proprietor interface 302, an example vector analyzer 304 and anexample audience determiner 306.

The example database proprietor interface 302 allows the AME 102 and/orthe audience metrics generator 112 to communicate with the firstdatabase proprietor 106A the second database proprietor 106B and/orother database proprietors. For example, the example database proprietorinterface 302 can be implemented via WAN hardware. In the illustratedexample of FIG. 3, the database proprietor interface 302 receives thefirst HLL data 132A from the first database proprietor 106A and thesecond HLL data 132B from the second database proprietor 106B. In someexamples, the database proprietor interface 302 can receive additionalHLL data from other database proprietors. In some examples, the databaseproprietor interface 302 sends any other suitable information to thedatabase proprietors 106A, 106B. In some examples, the databaseproprietor interface 302 can be absent. In such examples, the AME 102and/or the audience metrics generator 112 can communicate with thedatabase proprietors 106A, 106B via any other suitable method (e.g.,communicating via physical records, communicating via a third party,etc.).

The example vector analyzer 304 determines union HLL data based on thereceived HLL data 132A, 132B from the database proprietors 106A, 106B byperforming a union (e.g., combining, etc.) of the data sets of theaudiences represented via the HLL data 132A, 132B. For example, thevector analyzer 304 can sort the received HLL data 132A, 132B by theaddresses of the data bins and the uniqueness estimate values associatedtherewith. In some examples, the vector analyzer 304 can combine thesets by taking the max uniqueness estimate values associated with eachbin address. For example, for each bin address (e.g., a first set ofbits of the hash value associated with that bin, etc.), the vectoranalyzer 304 compares a unique estimate value of a bin in the first HLLdata 132A with a uniqueness estimate value of a corresponding bin withthe same bin address in the second HLL data 132B to determine theuniqueness estimate value that is the greatest or the maximum uniquenessestimate value between the two uniqueness estimate values. In thismanner, the example vector analyzer 304 compares uniqueness estimatevalues between same-address bins of the HLL data 132A, 132B to generatethe union HLL data based on the determined maximum uniqueness estimatevalues between the corresponding bins. Example functions of the vectoranalyzer 304 are described below in conjunction with FIGS. 4-6.

The example audience determiner 306 determines the total number ofdeduplicated unique audience members based on the union HLL data setgenerated by the vector analyzer 304. For example, the audiencedeterminer 306 can calculate the normalized harmonic mean of the unionHLL data. Example functions of the audience determiner 306 are describedbelow in conjunction with FIG. 6.

While an example manner of implementing the audience metrics generator112 of FIG. 1 is illustrated in FIG. 3, one or more of the elements,processes and/or devices illustrated in FIG. 3 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example database proprietor interface 302, the examplevector analyzer 304, the example audience determiner 306 and/or, moregenerally, the example audience metrics generator 112 of FIG. 3 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample database proprietor interface 302, the example vector analyzer304, the example audience determiner 306 and/or, more generally, theexample audience metrics generator 112 could be implemented by one ormore analog or digital circuit(s), logic circuits, programmableprocessor(s), programmable controller(s), graphics processing unit(s)(GPU(s)), digital signal processor(s) (DSP(s)), application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s))and/or field programmable logic device(s) (FPLD(s)). When reading any ofthe apparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example, databaseproprietor interface 302, the example vector analyzer 304, the exampleaudience determiner 306 is/are hereby expressly defined to include anon-transitory computer readable storage device or storage disk such asa memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. including the software and/or firmware. Further still, theexample audience metrics generator 112 of FIG. 3 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 3, and/or may include more than one of any or all ofthe illustrated elements, processes and devices. As used herein, thephrase “in communication,” including variations thereof, encompassesdirect communication and/or indirect communication through one or moreintermediary components, and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic intervals,scheduled intervals, aperiodic intervals, and/or one-time events.

FIG. 4 is an example diagram 400 illustrating generating of HLL data406A, 406B by the HLL generators 208A, 208B of FIG. 2, and processing ofthe HLL data 406A, 406B by the audience metrics generator 112 of FIGS. 1and 3. The example diagram 400 includes an example first PII set 402A,an example second PII set 402B, an example first hashed vector 404A, anexample second hashed values vector 404B, the example first HLL data406A, an example second HLL data 406B, example union HLL data 408, andan example HLL averaging function 410. In the illustrated example, thePII sets 402A, 402B, the hashed values vectors 404A, 404B, and the HLLdata 406A, 406B correspond to operations of the database proprietors106A, 106B. In the illustrated example, the union HLL data 408 and theHLL averaging function 410 correspond to operations of the AME 102.

The example PII sets 402A, 402B are PII information associated withdetected impressions. For example, the first example PII set 402Acorresponds to an impression detected by the first database proprietor106A and the second example PII set 402B corresponds to an impressiondetected by the second database proprietor 106B. In the illustratedexample, the example PII sets 402A, 402B include exclusively emailaddresses. In other examples, the PII sets 402A, 402B can include anyother PII information or combination of PII information. In someexamples, the PII sets 402A, 402B are retrieved from a databaseassociated with the first database proprietor 106A (e.g., the impressionand PII database 204A, etc.). In some examples, the PII information canbe appended with a salt (e.g., generated by the salt generator 206A,etc.).

The example hashed values vectors 404A, 404B (e.g., hash vectors, etc.)are generated when the HLL generators 208A, 208B process PII sets 402A,402B. In some examples, the HLL generators 208A, 208B hash each PII datainput in the PII sets 402A, 402B using an HLL algorithm. In otherexamples, the HLL generators 208A, 208B can use any other suitablealgorithm for generating the hashed values vectors 404A, 404B. In theillustrated example, the hashed values vectors 404A, 404B are depictedin binary. In other examples, the hashed values vectors 404A, 404B canbe stored in any other suitable manner (e.g., floating point, ASCII,etc.).

The example HLL data 406A, 406B are generated when the HLL generators208A, 208B store each bin address and uniqueness estimate of the hashedvalues vectors 404A, 404B. For example, the HLL generators 208A, 208Bcan also determine a uniqueness estimate corresponding to a likelihoodthat the value of the bin address contains at least one unique audiencemember. For example, the HLL generator 208A, 208B can determine themaximum number of leading zeros represented in the hashed values vectors404A, 404B. In the illustrated example of FIG. 4, the hashed valuesvector 404A is divided into a bin addresses “0” and “1,” whichcorrespond to the most significant bits (e.g., all bits by the last bit,etc.) of the hash values. In the illustrated example of FIG. 4, thefirst HLL generator 208A places the hash value “00” into the “0” binaddress because the hash value “00” has a leading bit of 0. In theillustrated example of FIG. 4, the first HLL generator 208A places thehash value “01” into the “0” bin address because the hash value “01” hasa leading bit of 0. The HLL generator 208A determines the uniquenessvalue of bin address “0” is “2” because the hash value “00” has twoleading zeros. The first HLL generator 208A determines that theuniqueness value of the “1” bin address is undefined because there areno hash values associated with the “1” bin address. In the illustratedexample, the undefined value in the first HLL data 406A is “-INF.” Inother examples, the undefined value can be any suitable value (e.g., −1,−10, null, etc.).

In the illustrated example of FIG. 4, the HLL generator 208B places thehash value “01” into the “0” bin address because the hash value “01” hasa leading bit of 0 (e.g., bin address=0). In the illustrated example ofFIG. 4, the HLL generator 208B places the hash value “11” into the “1”bin address because the hash value “11” has a leading bit of 1 (e.g.,bin address=1). The HLL generator 208B determines the uniqueness valueof bin address “0” is “1” because the hash value “01” has one leadingzeros. The HLL generator 208B determines that the uniqueness value ofthe “1” bin address is “0” because the hash value “11” has zero leadingzeros.

The example union HLL data 408 is based on the example HLL data 406A,406B after uniqueness estimate values are compared by the vectoranalyzer 304. In the illustrated example of FIG. 4, the vector analyzer304 determines the maximum uniqueness value associated with each binaddress of the HLL data 406A, 406B. For example, the “0” bin address hasa maximum uniqueness of “2” which is determined by the vector analyzer304 comparing the uniqueness estimate value of the “0” bin address ofthe first HLL data 406A (e.g., “2,” etc.) to the uniqueness estimatevalue of the “0” bin address of the second HLL data 406B (e.g., “1,”etc.). For example, the “1” bin address has a maximum uniqueness of “0”which is determined by the vector analyzer 304 comparing the uniquenessestimate value (e.g., “-INF”) of the “1” bin address of the first HLLdata 406A to the uniqueness estimate value (e.g., “0”) of the “1” binaddress of the second HLL data 406B.

The example HLL averaging function 410 determines the unique audiencecount (e.g., unique audience size) associated with the union HLL data408. For example, the HLL averaging function 410 can determine theharmonic mean of the union HLL data 408. In other examples, the HLLaveraging function 410 can determine the unique audience count by anyother suitable means. The functioning of the HLL averaging function 410is described below in greater detail with reference to FIG. 6.

FIG. 5 is another example diagram 500 illustrating generating of HLLdata 506A, 506B by the HLL generators 208A, 208B of FIG. 2, andprocessing of the HLL data 406A, 406B by the audience metrics generator102 of FIGS. 1 and 3. The example diagram 500 includes an example firstPII data set 502A, an example second PII data set 502B, an example firsthashed vector 504A, an example second hashed vector 504B, the examplefirst HLL data 506A, the example second HLL data 506B, an exampleunioned set address data structure 508, and an example HLL averagingfunction 510. In the illustrated example, the PII sets 502A, 502B, thehashed value vectors 504A, 504B, and the HLL data 506A, 506B correspondto operations of the database proprietors 106A, 106B. In the illustratedexample, the union HLL data 508 and the HLL averaging 510 correspond tooperations of the AME 102.

The example PII sets 502A, 502B are PII information associated withdetected impressions using database proprietor cookies. For example, thefirst example PII set 502A corresponds to an impression detected by thefirst database proprietor 106A using a cookie and the second example PIIset 502B corresponds to an impression detected by the second databaseproprietor 106B using a cookie. In the illustrated example, the examplePII sets 402A, 402B include exclusively database proprietor cookies. Inother examples, the PII sets 402A, 402B can include any other PIIinformation or combination of PII information. In some examples, the PIIsets 402A, 402B are retrieved from a database associated with the firstdatabase proprietor 106A (e.g., the impression and PII database 204A,etc.). In some examples, the PII information can be appended with a salt(e.g., generated by the salt generator 206A, etc.).

The example hashed values vectors 504A, 504B are generated when the HLLgenerators 208A, 208B process PII sets 502A, 502B. In some examples, theHLL generators 208A, 208B hash the each PII in the PII sets 502A, 502Busing an HLL algorithm. In other examples, the HLL generators 208 a,208B can use any other suitable algorithm for generating the hashedvalues vectors 504A, 504B. In the illustrated example, the hashed valuesvectors 504A, 504B are depicted in binary. In other examples, the hashedvalues vectors 504A, 504B can be stored in any other suitable manner(e.g., floating point, ASCII, etc.).

The example HLL data 506A, 506B are generated when the HLL generators208A, 208B store each bin address and uniqueness estimate of the hashedvalues vectors 504A, 504B. For example, the HLL generators 208A, 208Bcan also determine a uniqueness estimate corresponding to a likelihoodthat the value of the bin address contains at least one unique audiencemember. For example, the first HLL generator 208A, 208B can determinethe maximum number of leading zeros represented in the hashed valuesvectors 504A, 504B. In the illustrated example of FIG. 5, the hashedvector 504A is divided into a bin addresses “00,” “01,” “10,” and “11,”which correspond to the most significant bits (e.g., all bits by thelast bit, etc.) of the hash values. In the illustrated example of FIG.5, the first HLL generator 208A places the hash value “011” into the“01” bin address because the hash value “011” has a leading bit of “01.”In the illustrated example of FIG. 5, the HLL generator 208A places thehash value “110” into the “11” bin address because the hash value “110”has a leading bit of “11.” In the illustrated example of FIG. 5, the HLLgenerator 208A places the hash value “101” into the “10” bin addressbecause the hash value “101” has a leading bit of “10.” The HLLgenerator 208A determines that the uniqueness value of the “00” binaddress is undefined because there are no hash values associated withthe “00” bin address. The HLL generator 208A determines the uniquenessvalue of bin address “01” is “1” because the hash value “011” has oneleading zeros. The first HLL generator 208A determines the uniquenessvalue of bin address “10” is “0” because the hash value “101” has noleading zeros. The HLL generator 208A determines the uniqueness value ofbin address “11” is “0” because the hash value “101” has no leadingzeros.

In the illustrated example of FIG. 5, the hashed values vector 504B isdivided into a bin addresses “00,” “01,” “10,” and “11,” whichcorrespond to the most significant bits (e.g., all bits by the last bit,etc.) of the hash values. In the illustrated example of FIG. 5, the HLLgenerator 208B places the hash value “011” into the “01” bin addressbecause the hash value “011” has a leading bit of “01” (e.g., binaddress=01). In the illustrated example of FIG. 5, the HLL generator208B places the hash value “110” into the “11” bin address because thehash value “110” has a leading bit of “11” (e.g., bin address=11). Inthe illustrated example of FIG. 5, the HLL generator 208B places thehash value “101” into the “10” bin address because the hash value “101”has a leading bit of “10” (e.g., bin address=10). The HLL generator 208Bdetermines the uniqueness value of bin address “00” is “2” because thehash value “001” has two leading zeros. The HLL generator 208Bdetermines the uniqueness value of bin address “01” is “1” because thehash value “011” has one leading zeros. The HLL generator 208Bdetermines that the uniqueness value of the “10” bin address isundefined because there are no hash values associated with the “00” binaddress. The HLL generator 208B determines the uniqueness value of binaddress “11” is “0” because the hash value “110” has no leading zeros.In some examples, the address of the bin corresponds to the location ofthe hashed values vectors 504A, 504B.

The example union HLL data 508 is formed example HLL data 506A, 506Bafter uniqueness estimate values are compared by the vector analyzer304. In the illustrated example of FIG. 5, the vector analyzer 304determines the maximum uniqueness value associated with each bin addressof the HLL data 506A, 506B. For example, the “00” bin address has amaximum uniqueness of “2” which is determined by the vector analyzer 304comparing the uniqueness estimate value of the “00” bin address of thefirst HLL data 506A (e.g., “-INF,”) to the uniqueness estimate value ofthe “00” bin address of the HLL data structure 506B (e.g., “2,”). Forexample, the “01” bin address has a maximum uniqueness of “1” which isdetermined by the vector analyzer 304 comparing the uniqueness estimatevalue of the “01” bin address of the first bin address structure 506A(e.g., “1,”) to the uniqueness estimate value of the “01” bin address ofthe second bin address structure 506B (e.g., “1,”). For example, the“10” bin address has a maximum uniqueness of “0” which is determined bythe vector analyzer 304 comparing the uniqueness estimate value of the“10” bin address of the first bin address structure 506A (e.g., “0,”) tothe uniqueness estimate value of the “10” bin address of the first binaddress structure 506B (e.g., “-INF,”). For example, the “11” binaddress has a maximum uniqueness of “0” which is determined by thevector analyzer 304 comparing the uniqueness estimate value of the “1”bin address of the first HLL data 506A (e.g., “0,”) to the uniquenessestimate value of the “1” bin address of the second HLL data 506B (e.g.,“0,”).

The example HLL averaging function 510 determines the unique audiencecount (e.g., unique audience size) associated with the union HLL data508. For example, the HLL averaging function 510 can determine theharmonic mean of the unioned set. In other examples, the HLL averagingfunction 510 can determine the unique audience count by any othersuitable means. The functioning of the HLL averaging function 510 isdescribed below in greater detail with reference to FIG. 6.

FIG. 6 is an example diagram 600 illustrating a manner in which theaudience determiner of FIG. 3 determines a normalized harmonic meanbased on maximum uniqueness estimates of the HLL data from the from thedatabase proprietors of FIGS. 1 and 2. The example diagram 600 includesan example site data set 602, an example first hashed vector 604, anexample first bin address data structure 606, an example HLL data 608,and an example HLL averaging function 610.

The example site data set 602 is a data set including a plurality ofelements (e.g., “A,” “B,” “C,” “D,” etc.). The example data set 602 caninclude any suitable data (e.g., PII information, gathered cookies,etc.). In some examples, the data set 602 can be generated via theimpression detector 202A and/or impression and PII database 204A.

The example first hashed values vector 604 is generated when the exampledata set is hashed by the first HLL generator 208A. For example, thefirst HLL generator 208A can hash the data set 602 using an HLL hashingalgorithm, and/or any other suitable algorithm. In some examples, thefirst HLL generator 208A can appended the elements of the data set witha salt (e.g., a salt generated by the salt generator 206A, etc.). In theillustrated example, the values of the hashed vector 604 are in binary.In other examples, the hashed vector 604 can be formatted in anysuitable format (e.g., ASCII, etc.).

The example first HLL data 606 is generated after the first HLLgenerator 208A sorts each hash value of the hash vector 604 by the binaddress associated with each hash value. For example, the first HLLgenerator 208A determines the bin address of each value in the hashvector based on the leading bits of the hash value (e.g., the bits ofthe hash values excluding the least significant bit, etc.). In theillustrated example, the first HLL generator 208A sorts the “00” hashvalue into the “0” bin address based on the first bit of “00” is “0.”The HLL generator 208A sorts the “01” hash value into the “0” binaddress based on the first bit of “01” is “0.” The HLL generator 208Asorts the “10” hash value into the “1” bin address based on the firstbit of “10” is “1.” The HLL generator 208A sorts the “10” hash valueinto the “1” bin address based on the first bit of “11” is “1.” In otherexamples, the HLL generator 208A can determine what bin address to sortthe hash values into by any other suitable means.

The example second HLL data 608 is generated after the first HLLgenerator 208A determines the greatest uniqueness estimate of associatedwith each bin. In the illustrated example of FIG. 6, the first HLLgenerator 208A determines that maximum uniqueness associated with eachbin address. The first HLL generator 208A determines the greatestuniqueness among the hash values in each bin address in the first HLLdata 606. For example, the first HLL generator 208A determines theuniqueness of “0” address to be “2” based on “00” have the maximumnumber of leading zeros (e.g., 2 zeros, etc.). For example, the firstHLL generator 208A determines the uniqueness of “1” address to be “0”based on “10” have the maximum number of leading zeros (e.g., 0 zeros,etc.). In other examples, the first HLL generator 208A can determine theuniqueness estimate in any other suitable matter.

The example HLL averaging function 610 determines the estimated uniquecount (e.g., the estimate number of unique audience members, etc.) bydetermining the normalized harmonic mean of the second HLL data 608. Inother examples, the HLL averaging function 610 can use any othersuitable means of determining the estimated unique count of the uniqueaudience count.

FIG. 7 is an example graph 700 depicting the reduction of processingcomplexity using the examples described below in conjunction with FIGS.1-6. The example graph includes an example first axis 702, an examplesecond axis 704, an example first function 706, and an example secondfunction 708.

The first axis 702 is the y-axis of the example graph 700. In theillustrated example of FIG. 7, the first axis 702 corresponds to thenumber of mathematical operations required to determine estimate thenumber of unique audience members represented in a data set. In someexamples, the number of mathematical operations corresponds to thememory and/or processing burdening corresponding to a given operation.Accordingly, a lower value (e.g., a lower location, etc.) on the firstaxis 702 correspond to a reduction of memory/or processing burden of thefunction.

The second axis 704 is the x-axis of the example graph 700. In theillustrated example of FIG. 8, the second axis 704 corresponds to thenumber of unique elements (e.g., the number of bins, etc.) in the datato be deduplicated. Accordingly, a higher value (e.g., a locationfurther to the left, etc.) on the second axis 704 corresponds to anincrease in the number of elements to be deduplicated.

The example first function 706 correspond to traditional means fordeduplicating sets of data. For example, the first function 706 cancorrespond to the HashSet method. In other examples, the first function706 can correspond to any other suitable method. In the illustratedexample of FIG. 7, as the number of unique elements in the setsincreases, the number of operations required for the first function 706deduplicates the data increases linearly. The example second function708 corresponds to the example disclosed above in conjunction with FIGS.1-6. In the illustrated example of FIG. 7, as the number of uniqueelements in the sets increases, the number of operations required forthe first function 706 deduplicates the data increases logarithmically.Accordingly, the second function 708 dramatically reduces the memoryand/or processing requirements to deduplicate data when compared to thefirst function 706.

As used herein, the phrase “in communication,” including variationsthereof, encompasses direct communication and/or indirect communicationthrough one or more intermediary components, and does not require directphysical (e.g., wired) communication and/or constant communication, butrather additionally includes selective communication at periodicintervals, scheduled intervals, aperiodic intervals, and/or one-timeevents.

A flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the example first databaseproprietor 106A of FIGS. 1-2 is shown in FIG. 8. The machine-readableinstructions may be one or more executable programs or portion(s) of anexecutable program for execution by a computer processor such as theprocessor 1012 shown in the example processor platform 1000 discussedbelow in connection with FIG. 10. The program may be embodied insoftware stored on a non-transitory computer readable storage mediumsuch as a CD-ROM, a floppy disk, a hard drive, a DVD, a Blu-ray disk, ora memory associated with the processor 1112, but the entire programand/or parts thereof could alternatively be executed by a device otherthan the processor 1112 and/or embodied in firmware or dedicatedhardware. Further, although the example program is described withreference to the flowchart illustrated in FIG. 8, many other methods ofimplementing the example first database proprietor 106A mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined. Additionally or alternatively, any or all ofthe blocks may be implemented by one or more hardware circuits (e.g.,discrete and/or integrated analog and/or digital circuitry, an FPGA, anASIC, a comparator, an operational-amplifier (op-amp), a logic circuit,etc.) structured to perform the corresponding operation withoutexecuting software or firmware.

FIG. 8 includes a process 800 that begins at block 802. At block 802,the impression detector 202A gathers user impression data. For example,the impression detector 202A can detect an impression request (e.g., theimpression request 122A, etc.) created by a client device (e.g., one ofthe client devices 108, etc.) in response to the client device accessingmedia. In some examples, the impression identifier 112A can include anyinformation usable to identify the media and/or the requesting clientdevice. In some examples, the impression identifier 122A can includerelevant PII associated with a user of the client device. In someexamples, the impression identifier 122A can include information thefirst database proprietor 106A can use to link PII information to thegathered impression,

At block 804, the first salt generator 206A generates a salt based oncampaign information and/or a date range. For example, the first saltgenerator 206A can use a campaign attribute (e.g., a date rangeassociated with the campaign, etc.) to generate a salt for use increating the HLL data. In other examples, the first salt generator 206Acan use any other information suitable data (e.g., any arbitrary data,etc.) for use in generating the salt.

At block 806, the first salt encryptor 210A encrypts the salt with anencryption key. The first salt encryptor 210A encrypts the generate saltusing an encryption key associated with the first database proprietor106A and/or the second database proprietor 106B. In some examples, theencryption key is provided by a third party (e.g., the AME 102, etc.) toencrypted the salt key. In some examples, the execution of block 806 canbe omitted. In such examples, the generated salt is not encrypted.

At block 808, the first database proprietor interface 214A provides theencryption key to other database proprietor(s). From example, the firstsalt encryptor 210A can use the first database proprietor interface 214Ato transmit the encryption key to the second database proprietor 106B.In some examples, the execution of block 808 can be omitted. Forexample, the encryption key can be a public key. In other examples, theencryption key can be transmitted by a third party (e.g., the AME 102,etc.) to the other database proprietor 102B.

At block 810, the first database proprietor interface 214A provides saltto other database proprietors. From example, the first salt encryptor210A and/or the first salt generator 206A can use the first databaseproprietor interface 214A to transmit the generated salt to the seconddatabase proprietor 106B. In some examples, the generated salt is nottransmitted to the AME 102 to ensure the privacy of the PII informationassociated with the gathered impressions.

At block 812, the first HLL generator 208A generates an HLL usinggenerated salt and user impression data. For example, the first HLLgenerator 208A can use the salt generated by the first salt generator206A and a PII associated with each of the detected impression. Forexample, the first HLL generator 208A can concatenate a PII (e.g., anemail address) with the salt, hash the concatenated data, and add theresulting hash value to the HLL data 132A (e.g., a vector, a matrix,etc.). In some examples, the first HLL generator 208A can generate theHLL data 132A based on each impression stored in the impression and PIIdatabase 204A and associated with a particular time period (e.g., a day,a week, etc.). In such examples, the HLL data 132A is generated usingthe same salt. An example of the function of the first HLL generator208A is described above in conjunction with FIGS. 4-6.

At block 814, the first AME interface 212A transmits HLL to the audiencemeasurement entity 102. For example, the first HLL generator 208A cancause the first AME interface 212A to transmit the generated HLL data132A to the AME 102. In some examples, the first AME interface 212A doesnot transmit the generated salt to the AME 102. In such examples, theAME 102 is unable to determine the PII information encoded in the HLLdata 132A

At block 816, the first HLL generator 208A determines if another HLL isto be generated. For example, the first HLL generator 208A can determineif there is additional impression data stored in the impression and PIIdatabase 204A. In some examples, the first HLL generator 208A canoperate periodically (e.g., every hour, every day, etc.). If the firstHLL generator 208A decides another HLL is too be generated, the process800 advances to block 818. If the first HLL generator 208A decidesanother HLL is not be generated, the process 800 ends.

At block 818, the first salt generator 206A decides if the salt is to berotated. For example, the first salt generator 206A can generated a newsalt periodically (e.g., every day, etc.). In other examples, the firstsalt generator 206A can generate a new salt at the beginning of a newadvertisement campaign. In some examples, the first salt generator 206Aafter receiving a notification from the AME 102. If the first saltgenerator 206A decides the salt is to be rotated, the process 800returns to block 804. If the first salt generator 206A decides the saltis not to be rotated, the process 800 returns to block 812.

A flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the example first audience entityof FIGS. 1 and 3 is shown in FIG. 9. The machine-readable instructionsmay be one or more executable programs or portion(s) of an executableprogram for execution by a computer processor such as the processor 1112shown in the example processor platform 1100 discussed below inconnection with FIG. 11. The program may be embodied in software storedon a non-transitory computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associatedwith the processor 1112, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor1112 and/or embodied in firmware or dedicated hardware. Further,although the example program is described with reference to theflowchart illustrated in FIG. 9, many other methods of implementing theexample first database proprietor 106A may alternatively be used. Forexample, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, or combined.Additionally or alternatively, any or all of the blocks may beimplemented by one or more hardware circuits (e.g., discrete and/orintegrated analog and/or digital circuitry, an FPGA, an ASIC, acomparator, an operational-amplifier (op-amp), a logic circuit, etc.)structured to perform the corresponding operation without executingsoftware or firmware.

The machine readable instructions described herein may be stored in oneor more of a compressed format, an encrypted format, a fragmentedformat, a compiled format, an executable format, a packaged format, etc.Machine readable instructions as described herein may be stored as data(e.g., portions of instructions, code, representations of code, etc.)that may be utilized to create, manufacture, and/or produce machineexecutable instructions. For example, the machine readable instructionsmay be fragmented and stored on one or more storage devices and/orcomputing devices (e.g., servers). The machine readable instructions mayrequire one or more of installation, modification, adaptation, updating,combining, supplementing, configuring, decryption, decompression,unpacking, distribution, reassignment, compilation, etc. in order tomake them directly readable, interpretable, and/or executable by acomputing device and/or other machine. For example, the machine readableinstructions may be stored in multiple parts, which are individuallycompressed, encrypted, and stored on separate computing devices, whereinthe parts when decrypted, decompressed, and combined form a set ofexecutable instructions that implement a program such as that describedherein.

In another example, the machine readable instructions may be stored in astate in which they may be read by a computer, but require addition of alibrary (e.g., a dynamic link library (DLL)), a software development kit(SDK), an application programming interface (API), etc. in order toexecute the instructions on a particular computing device or otherdevice. In another example, the machine readable instructions may needto be configured (e.g., settings stored, data input, network addressesrecorded, etc.) before the machine readable instructions and/or thecorresponding program(s) can be executed in whole or in part. Thus, thedisclosed machine readable instructions and/or corresponding program(s)are intended to encompass such machine readable instructions and/orprogram(s) regardless of the particular format or state of the machinereadable instructions and/or program(s) when stored or otherwise at restor in transit.

The machine readable instructions described herein can be represented byany past, present, or future instruction language, scripting language,programming language, etc. For example, the machine readableinstructions may be represented using any of the following languages: C,C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language(HTML), Structured Query Language (SQL), Swift, etc.

As mentioned above, the example processes of FIGS. 8 and 9 may beimplemented using executable instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

As used herein, singular references (e.g., “a”, “an”, “first”, “second”,etc.) do not exclude a plurality. The term “a” or “an” entity, as usedherein, refers to one or more of that entity. The terms “a” (or “an”),“one or more”, and “at least one” can be used interchangeably herein.Furthermore, although individually listed, a plurality of means,elements or method actions may be implemented by, e.g., a single unit orprocessor. Additionally, although individual features may be included indifferent examples or claims, these may possibly be combined, and theinclusion in different examples or claims does not imply that acombination of features is not feasible and/or advantageous.

FIG. 9 includes a process 900 that begins at block 902. At block 902,the database proprietor interface 302 receives HLL data 132A, 132B fromthe database proprietors 106A, 106B. For example, the databaseproprietor interface 302 can receive one or more hashed vectorscontaining hashed vectors. In some examples, the database proprietorinterface 302 can request the HLL data 132A, 132B periodically from thedatabase proprietors 106A, 106B. In other examples, the databaseproprietor interface 302 can receive the HLL data 132A, 132B from thedatabase proprietors 106A, 106B as they HLL data 132A, 132B isgenerated,

At block 904, the vector analyzer 304 combines the HLL vectors to unionthe sets of audiences in the HLL vectors. For example, the vectoranalyzer 304 can use the process described above in conjunction withFIGS. 4-6 to combine the received HLL data 132A, 132B. In otherexamples, the vector analyzer 304 can use any other suitable means tocombine the received HLL vectors.

At block 906, audience determiner 306 determines the total number ofdeduplicated unique audience members based on the unionid sets. Forexample, the audience determiner 306 can use the process described inconjunction to determine the unique audience count 318. In otherexamples, the audience determiner 306 can use any other suitable meansto determine the unique audience estimate 318. The process 900 thenends.

FIG. 10 is a block diagram of an example processor platform 1000structured to execute the instructions of FIG. 8 to implement theexample first database proprietor 106A and/or the second databaseproprietor 106B of FIGS. 1-2. The processor platform 1000 can be, forexample, a server, a personal computer, a workstation, a self-learningmachine (e.g., a neural network), or any other type of computing device.

The processor platform 1000 of the illustrated example includes aprocessor 1012. The processor 1012 of the illustrated example ishardware. For example, the processor 1012 can be implemented by one ormore integrated circuits, logic circuits, microprocessors, GPUs, DSPs,or controllers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example first impressiondetector 202A, the example first salt generator 206A, an example firstHLL generator 208A, and the example first salt encryptor 210A.

The processor 1012 of the illustrated example includes a local memory1013 (e.g., a cache). The processor 1012 of the illustrated example isin communication with a main memory including a volatile memory 1014 anda non-volatile memory 1016 via a bus 1018. The volatile memory 1014 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random AccessMemory (RDRAM®) and/or any other type of random access memory device.The non-volatile memory 1016 may be implemented by flash memory and/orany other desired type of memory device. Access to the main memory 1014,1016 is controlled by a memory controller.

The processor platform 1000 of the illustrated example also includes aninterface circuit 1020. The interface circuit 1020 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface. In theillustrated example, the interface circuit 1020 implements the examplefirst AME interface 212A, and the example first database proprietorinterface 214A.

In the illustrated example, one or more input devices 1022 are connectedto the interface circuit 1020. The input device(s) 1022 permit(s) a userto enter data and/or commands into the processor 1012. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 1024 are also connected to the interfacecircuit 1020 of the illustrated example. The output devices 1024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 1020 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 1020 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 1026. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 1000 of the illustrated example also includes oneor more mass storage devices 1028 for storing software and/or data.Examples of such mass storage devices 1028 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives. In the illustrated example, the mass storage device(s)implements the example first impression and PII database 204A.

Machine-executable instructions 1032 represented by the flowchart ofFIG. 8 may be stored in the mass storage device 1028, in the volatilememory 1014, in the non-volatile memory 1016, and/or on a removablenon-transitory computer readable storage medium such as a CD or DVD.

FIG. 11 is a block diagram of an example processor platform 1100structured to execute the instructions of FIG. 9 to implement theexample audience measurement entity 102 of FIGS. 1 and 3. The processorplatform 1100 can be, for example, a server, a personal computer, aworkstation, a self-learning machine (e.g., a neural network), or anyother type of computing device.

The processor platform 1100 of the illustrated example includes aprocessor 1112. The processor 1112 of the illustrated example ishardware. For example, the processor 1112 can be implemented by one ormore integrated circuits, logic circuits, microprocessors, GPUs, DSPs,or controllers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example vector analyzer 304and the example audience determiner 306.

The processor 1112 of the illustrated example includes a local memory1113 (e.g., a cache). The processor 1112 of the illustrated example isin communication with a main memory including a volatile memory 1114 anda non-volatile memory 1116 via a bus 1118. The volatile memory 1114 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random AccessMemory (RDRAM®) and/or any other type of random access memory device.The non-volatile memory 1116 may be implemented by flash memory and/orany other desired type of memory device. Access to the main memory 1114,1116 is controlled by a memory controller.

The processor platform 1100 of the illustrated example also includes aninterface circuit 1120. The interface circuit 1120 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 1122 are connectedto the interface circuit 1120. The input device(s) 1122 permit(s) a userto enter data and/or commands into the processor 1112. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 1124 are also connected to the interfacecircuit 1120 of the illustrated example. The output devices 1124 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 1120 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 1120 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 1126. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc. In the illustrated example, the interface circuit 1120implements the database proprietor interface 302.

The processor platform 1100 of the illustrated example also includes oneor more mass storage devices 1128 for storing software and/or data.Examples of such mass storage devices 1128 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

Machine-executable instructions 1132 represented by the flowchart ofFIG. 9 may be stored in the mass storage device 1128, in the volatilememory 1114, in the non-volatile memory 1116, and/or on a removablenon-transitory computer readable storage medium such as a CD or DVD.

Example methods, apparatus, systems, and articles of manufacture togenerate measures of unique audiences for Internet-based media aredisclosed herein. Further examples and combinations thereof include thefollowing: Example 1 includes an apparatus comprising a salt generatorto generate a shared salt at a first database proprietor, a databaseproprietor interface to the shared salt to a second database proprietor,a Hyperloglog (HLL) generator to generate a first hash vector at thefirst database proprietor based on the shared salt and first userimpression data associated with media, the first hash vector toobfuscate first personally identifiable information of first subscribersof the first database proprietor, and an AME interface to send the firsthash vector to a database containing a second hash vector, the secondhash vector generated by the second database proprietor using seconduser impression data and the shared salt, the second hash vector toobfuscate second personally identifiable information of secondsubscribers of the second database proprietor, the first hash vector andthe second hash vector to enable a third party to a deduplicate audiencesize corresponding to the first user impression data and the second userimpression data.

Example 2 includes the apparatus of example 1, wherein the media is anadvertising campaign.

Example 3 includes the apparatus of example 1, wherein the saltgenerator generates the shared salt based on a first campaign attributeof the media, and further including the salt generator to determine asecond advertising campaign attribute, the salt generator to determine asecond shared salt based on the second advertising campaign attribute,and the database proprietor interface to send the second shared salt tothe second database proprietor.

Example 4 includes the apparatus of example 1, further including a saltencryptor to encrypt the shared salt with an encryption key, theencryption key available to the first and second database proprietors.

Example 5 includes the apparatus of example 1, wherein the third partyis an audience measurement entity.

Example 6 includes the apparatus of example 1, wherein the AME interfacesending the first hash vector to the database includes sending the firsthash vector to a cloud server.

Example 7 includes the apparatus of example 1, wherein the first hashvector is a first hyperloglog vector, the second hash vector is a secondhyperloglog vector, and the third party unions the first hyperloglogvector and the second hyperloglog vector to determine the deduplicatedaudience size.

Example 8 includes a method comprising generating a shared salt at afirst database proprietor, sending the shared salt to a second databaseproprietor, generating a first hash vector at the first databaseproprietor based on the shared salt and first user impression dataassociated with media, the first hash vector to obfuscate firstpersonally identifiable information of first subscribers of the firstdatabase proprietor, and sending the first hash vector to a databasecontaining a second hash vector, the second hash vector generated by thesecond database proprietor using second user impression data and theshared salt, the second hash vector to obfuscate second personallyidentifiable information of second subscribers of the second databaseproprietor, the first hash vector and the second hash vector to enable athird party to a deduplicate audience size corresponding to the firstuser impression data and the second user impression data.

Example 9 includes the method of example 8, wherein the media is anadvertising campaign.

Example 10 includes the method of example 8, wherein the shared salt isbased or a first attribute of the media, and further includingdetermining a second advertising campaign attribute, determining asecond shared salt based on the second advertising campaign attribute,and sending the second shared salt to the second database proprietor.

Example 11 includes the method of example 8, further includingencrypting the shared salt with an encryption key, the encryption keyavailable to the first and second database proprietors.

Example 12 includes the method of example 8, wherein the third party isan audience measurement entity.

Example 13 includes the method of example 8, wherein sending the firsthash vector to the database includes sending the first hash vector to acloud server.

Example 14 includes the method of example 8, wherein the first hashvector is a first hyperloglog vector, the second hash vector is a secondhyperloglog vector, and the third party unions the first hyperloglogvector and the second hyperloglog vector to determine the deduplicatedaudience size.

Example 15 includes a non-transitory computer readable storage mediumcomprising instructions that, when executed, cause a processor to atleast generate a shared salt at a first database proprietor, send theshared salt to a second database proprietor, generate a first hashvector at the first database proprietor based on the shared salt andfirst user impression data associated with media, the first hash vectorto obfuscate first personally identifiable information of firstsubscribers of the first database proprietor, and send the first hashvector to a database containing a second hash vector, the second hashvector generated by the second database proprietor using second userimpression data and the shared salt, the second hash vector to obfuscatesecond personally identifiable information of second subscribers of thesecond database proprietor, the first hash vector and the second hashvector to enable a third party to a deduplicate audience sizecorresponding to the first user impression data and the second userimpression data.

Example 16 includes the non-transitory computer readable storage mediumof example 15, wherein the media is an advertising campaign.

Example 17 includes the non-transitory computer readable storage mediumof example 15, wherein the shared salt is based or a first attribute ofthe media, and the instructions further cause the processor to determinea second advertising campaign attribute, determine a second shared saltbased on the second advertising campaign attribute, and send the secondshared salt to the second database proprietor.

Example 18 includes the non-transitory computer readable storage mediumof example 15, wherein the instructions further cause the processor toencrypt the shared salt with an encryption key, the encryption keyavailable to the first and second database proprietors.

Example 19 includes the non-transitory computer readable storage mediumof example 15, wherein the third party is an audience measuremententity.

Example 20 includes the non-transitory computer readable storage mediumof example 15, wherein the first hash vector is a first hyperloglogvector, the second hash vector is a second hyperloglog vector, and thethird party unions the first hyperloglog vector and the secondhyperloglog vector to determine the deduplicated audience size.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that determinea unique audience for internet-based media. The disclosed methods,apparatus and articles of manufacture improve the efficiency of using acomputing device by reducing the amount of processing and memoryrequired to determine the unique audience based on detected impressions.The disclosed methods, apparatus and articles of manufacture areaccordingly directed to one or more improvement(s) in the functioning ofa computer.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

The following claims are hereby incorporated into this DetailedDescription by this reference, with each claim standing on its own as aseparate embodiment of the present disclosure.

The invention claimed is:
 1. An apparatus comprising: at least onememory; instructions; and processor circuitry to execute theinstructions to: receive first HyperLogLog (HLL) data from a firstserver of a first database proprietor and second HyperLogLog (HLL) datafrom a second server of a second database proprietor, the first HLL dataincluding obfuscated first user impression data and the second HLL dataincluding obfuscated second user impression data; generate union HLLdata based on the first HLL data from the first database proprietor andthe second HLL data from the second database proprietor by performing aunion of data sets of the obfuscated first user impression datarepresented in the first HLL data and the obfuscated second userimpression data represented in the second HLL data; and determine atotal number of deduplicated unique audience members based on the unionHLL data.
 2. The apparatus as defined in claim 1, wherein the first HLLdata includes first hashed vectors, the first hashed vectors includingfirst hashed values sorted by bin addresses associated with hash valuesof the first hashed vectors, the second HLL data including second hashedvectors, the second hashed vectors including second hashed values sortedby bin addresses associated with hash values of the second hashedvectors.
 3. The apparatus as defined in claim 2, wherein to generate theunion HLL data, the processor circuitry is to execute the instructionsto sort uniqueness estimate values associated with the first hashedvectors of the first HLL data and the second hashed vectors of thesecond HLL data.
 4. The apparatus as defined in claim 3, wherein to sortthe uniqueness estimate values, the processor circuitry is to executethe instructions to compare a first uniqueness estimate value of a firstbin address of the first HLL data with a second uniqueness estimatevalue of a second bin address of the second HLL data, the first binaddress being the same as the second bin address.
 5. The apparatus asdefined in claim 2, wherein the processor circuitry is to execute theinstructions to generate the union HLL data based on a maximumuniqueness value associated with the first hashed vectors of the firstHLL data and the second hashed vectors of the second HLL data.
 6. Theapparatus as defined in claim 5, wherein to determine the maximumuniqueness value, the processor circuitry is to execute the instructionsto compare a first uniqueness estimate value of a first bin address ofthe first HLL data and a second uniqueness estimate value of a secondbin address of the second HLL data, the first bin address being the sameas the second bin address.
 7. The apparatus as defined in claim 2,wherein to generate the union HLL data, the processor circuitry is toexecute the instructions to combine the first HLL hash vectors and thesecond HLL hash vectors to union sets of audiences in the first andsecond HLL vectors.
 8. The apparatus as defined in claim 1, wherein theprocessor circuitry is to execute the instructions to determine thetotal number of deduplicated unique audience members by obtaining aharmonic mean of the union HLL data.
 9. A non-transitory computerreadable storage medium comprising instructions that, when executed,cause at least one processor to at least: receive first HyperLogLog(HLL) data from a first server of a first database proprietor and secondHyperLogLog (HLL) data from a second server of a second databaseproprietor, the first HLL data including obfuscated first userimpression data and the second HLL data including obfuscated second userimpression data; generate union HLL data based on the first HLL datafrom the first database proprietor and the second HLL data from thesecond database proprietor by performing a union of data sets of theobfuscated first user impression data represented in the first HLL dataand the obfuscated second user impression data represented in the secondHLL data; and determine a total number of deduplicated unique audiencemembers based on the union HLL data.
 10. The non-transitory computerreadable storage medium of claim 9, wherein the first HLL data includesfirst hashed vectors, the first hashed vectors including first hashedvalues sorted by bin addresses associated with hash values of the firsthashed vectors, the second HLL data includes second hashed vectors, thesecond hashed vectors having second hashed values sorted by binaddresses associated with hash values of the second hashed vectors. 11.The non-transitory computer readable storage medium of claim 10, whereinto generate the union HLL data, the at least one processor is to sortuniqueness estimate values associated with the first hashed vectors ofthe first HLL data and the second hashed vectors of the second HLL data.12. The non-transitory computer readable storage medium of claim 11,wherein to sort the uniqueness estimate values, the at least oneprocessor is to compare a first uniqueness estimate value of a first binaddress of the first HLL data with a second uniqueness estimate value ofa second bin address of the second HLL data, the first bin address beingthe same as the second bin address.
 13. The non-transitory computerreadable storage medium of claim 10, wherein the at least one processoris to generate the union HLL data based on a maximum uniqueness valueassociated with the first hashed vectors of the first HLL data and thesecond hashed vectors of the second HLL data.
 14. The non-transitorycomputer readable storage medium of claim 13, wherein to determine themaximum uniqueness value, the at least one processor is to compare afirst uniqueness estimate value of a first bin address of the first HLLdata and a second uniqueness estimate value of a second bin address ofthe second HLL data, the first bin address being the same as the secondbin address.
 15. The non-transitory computer readable storage medium ofclaim 10, wherein the at least one processor is to combine the first HLLhash vectors and the second HLL hash vectors to union sets of audiencesin the first and second HLL vectors to generate the union HLL data. 16.The non-transitory computer readable storage medium of claim 9, whereinthe at least one processor is to obtain a harmonic mean of the union HLLdata to determine the total number of deduplicated unique audiencemembers.
 17. A method comprising: accessing first HyperLogLog (HLL) datafrom a first server of a first database proprietor and secondHyperLogLog (HLL) data from a second server of a second databaseproprietor, the first HLL data including obfuscated first userimpression data and the second HLL data including obfuscated second userimpression data; generating, by executing an instruction with aprocessor, union HLL data based on the first HLL data from the firstdatabase proprietor and the second HLL data from the second databaseproprietor by performing a union of data sets of the obfuscated firstuser impression data represented in the first HLL data and theobfuscated second user impression data represented in the second HLLdata; and determining, by executing an instruction with a processor, atotal number of deduplicated unique audience members based on the unionHLL data.
 18. The method as defined in claim 17, wherein the first HLLdata includes first hashed vectors, the first hashed vectors includingfirst hashed values sorted by bin addresses associated with hash valuesof the first hashed vectors, the second HLL data includes second hashedvectors, the second hashed vectors including second hashed values sortedby bin addresses associated with hash values of the second hashedvectors.
 19. The method as defined in claim 18, wherein the generatingof the union HLL data includes sorting uniqueness estimate valuesassociated with the first hashed vectors of the first HLL data and thesecond hashed vectors of the second HLL data by comparing a firstuniqueness estimate value of a first bin address of the first HLL datawith a second uniqueness estimate value of a second bin address of thesecond HLL data, the first bin address being the same as the second binaddress.
 20. The method as defined in claim 18, wherein the generatingof the union HLL data includes generating the union HLL data based on amaximum uniqueness value associated with the first hashed vectors of thefirst HLL data and the second hashed vectors of the second HLL data bycomparing a first uniqueness estimate value of a first bin address ofthe first HLL data and a second uniqueness estimate value of a secondbin address of the second HLL data, the first bin address being the sameas the second bin address.
 21. The method as defined in claim 18,wherein the generating of the union HLL data includes combining thefirst HLL hash vectors and the second HLL hash vectors to union sets ofaudiences in the first and second HLL vectors.
 22. The method as definedin claim 17, wherein audience determiner determines the total number ofdeduplicated unique audience members by obtaining a harmonic mean of theunion HLL data.
 23. An apparatus comprising: a database proprietorinterface to receive first HyperLogLog (HLL) data from a first server ofa first database proprietor and second HyperLogLog (HLL) data from asecond server of a second database proprietor, the first HLL dataincluding obfuscated first user impression data and the second HLL dataincluding obfuscated second user impression data; a vector analyzercircuitry to generate union HLL data based on the first HLL data fromthe first database proprietor and the second HLL data from the seconddatabase proprietor by performing a union of data sets of the obfuscatedfirst user impression data represented in the first HLL data and theobfuscated second user impression data represented in the second HLLdata; and an audience determiner circuitry to determine a total numberof deduplicated unique audience members based on the union HLL datagenerated by the vector analyzer.
 24. The apparatus as defined in claim23, wherein the first HLL data includes first hashed vectors, the firsthashed vectors including first hashed values sorted by bin addressesassociated with hash values of the first hashed vectors, the second HLLdata includes second hashed vectors, the second hashed vectors includingsecond hashed values sorted by bin addresses associated with hash valuesof the second hashed vectors.
 25. The apparatus as defined in claim 24,wherein to generate the union HLL data, the vector analyzer circuitry isto sort uniqueness estimate values associated with the first hashedvectors of the first HLL data and the second hashed vectors of thesecond HLL data.
 26. The apparatus as defined in claim 25, wherein tosort the uniqueness estimate values, the vector analyzer circuitry is tocompare a first uniqueness estimate value of a first bin address of thefirst HLL data with a second uniqueness estimate value of a second binaddress of the second HLL data, the first bin address being the same asthe second bin address.
 27. The apparatus as defined in claim 24,wherein the vector analyzer circuitry is to generate the union HLL databased on a maximum uniqueness value associated with the first hashedvectors of the first HLL data and the second hashed vectors of thesecond HLL data.
 28. The apparatus as defined in claim 27, wherein todetermine the maximum uniqueness value, the vector analyzer compares afirst uniqueness estimate value of a first bin address of the first HLLdata and a second uniqueness estimate value of a second bin address ofthe second HLL data, the first bin address being the same as the secondbin address.
 29. The apparatus as defined in claim 24, wherein togenerate the union HLL data, the vector analyzer circuitry is to combinethe first HLL hash vectors and the second HLL hash vectors to union setsof audiences in the first and second HLL vectors.
 30. The apparatus asdefined in claim 23, wherein the audience determiner circuitry is todetermine the total number of deduplicated unique audience members byobtaining a harmonic mean of the union HLL data.